Demographics and reproductive biology of H. schistosus from bycatch on the west coast of India
Analysis for Manuscript
1 Demographics of H. schistosus across time
1.1 Sample size
snakes%>%
group_by(Species)%>%
count(name = "N")| Species | N |
|---|---|
| Hydrophis curtus | 282 |
| Hydrophis schistosus | 967 |
snakes%>%
filter(Gear.Type != "")%>%
select(Date, Gear.Type, No..of.Hauls, Average.Haul.Duration..Hours., Tow.duration.hours)%>%
distinct()%>%
mutate(Tow.duration.hours = case_when(!is.na(Tow.duration.hours) ~ Tow.duration.hours,
is.na(Tow.duration.hours) ~ No..of.Hauls*Average.Haul.Duration..Hours.))%>%
group_by(Gear.Type)%>%
summarise(n = n(),
haul.hours = sum(Tow.duration.hours, na.rm = T))| Gear.Type | n | haul.hours |
|---|---|---|
| GillNet | 340 | 535.9633 |
| Rampan | 46 | 190.4500 |
| Trawler | 76 | 285.1600 |
1.2 Age structure
age.yr <- snakes%>%
group_by(Species, Year)%>%
summarise(N = n(),
mean = mean(Snout.to.Vent..cm., na.rm = T))
maturtity <- snakes%>%
group_by(Species)%>%
count()%>%
mutate(juv = 35,
adult = ifelse(Species == "Hydrophis curtus", 54, 65))# plotting distribution of SVL across years
snakes%>%
filter(Snout.to.Vent..cm. > 20)%>%
ggplot(aes(Year, Snout.to.Vent..cm.))+
geom_violin(fill = "grey")+
geom_boxplot(width = 0.1)+
geom_hline(data = maturtity, aes(yintercept = adult), linetype = "dashed")+
geom_hline(data = maturtity, aes(yintercept = juv), linetype = "dotted")+
#stat_summary(fun.data = "mean_sdl", geom = "pointrange", size = 1)+
geom_label(data = age.yr, aes(Year, 10, label = N))+
facet_wrap(~Species, ncol = 1, scales = "free_y")+
labs(y = "Snout to vent length (cm)")+
theme(strip.text = element_text(face = "italic"))
ggsave(last_plot(), filename = "./Figures/figure1.tiff", height = 6, width = 8)pop.yr <- snakes%>%
filter(!is.na(Class))%>% # fix 2016 svl
group_by(Species, Year)%>%
count(Class)%>%
complete(Class, fill = list(n = 0))%>%
mutate(N = sum(n))%>%
group_by(Year, Species, Class)%>%
mutate(prop.age = n/N)
pop.yr%>%
group_by(Species, Class)%>%
skimr::skim(prop.age)%>%
skimr::yank("numeric")%>%
select(-hist)Variable type: numeric
| skim_variable | Species | Class | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| prop.age | Hydrophis curtus | Adult | 0 | 1 | 0.43 | 0.31 | 0.17 | 0.28 | 0.33 | 0.49 | 0.88 |
| prop.age | Hydrophis curtus | Juvenile | 0 | 1 | 0.57 | 0.31 | 0.12 | 0.50 | 0.66 | 0.72 | 0.83 |
| prop.age | Hydrophis curtus | Neonate | 0 | 1 | 0.02 | NA | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 |
| prop.age | Hydrophis schistosus | Adult | 0 | 1 | 0.88 | 0.09 | 0.79 | 0.84 | 0.87 | 0.91 | 1.00 |
| prop.age | Hydrophis schistosus | Juvenile | 0 | 1 | 0.14 | 0.07 | 0.08 | 0.10 | 0.12 | 0.17 | 0.21 |
| prop.age | Hydrophis schistosus | Neonate | 0 | 1 | 0.03 | 0.02 | 0.01 | 0.02 | 0.03 | 0.03 | 0.04 |
pop.yr%>%
filter(Year != 2019)%>%
group_by(Species, Class)%>%
nest()%>%
mutate(test = map(data, ~prop.test(.$n, .$N)),
sumr = map(test, broom::tidy))%>%
select(sumr)%>%
unnest()%>%
select(Class:p.value)| Species | Class | estimate1 | estimate2 | estimate3 | statistic | p.value |
|---|---|---|---|---|---|---|
| Hydrophis curtus | Adult | 0.8750000 | 0.3125000 | 0.3551402 | 9.2546983 | 0.0097807 |
| Hydrophis curtus | Juvenile | 0.1250000 | 0.6875000 | 0.6261682 | 8.8509715 | 0.0119684 |
| Hydrophis curtus | Neonate | NA | NA | NA | 97.2336449 | 0.0000000 |
| Hydrophis schistosus | Adult | 0.8846154 | 0.7894737 | 0.8613445 | 5.9949721 | 0.0499124 |
| Hydrophis schistosus | Juvenile | 0.0769231 | 0.2105263 | 0.1239496 | 9.7219255 | 0.0077430 |
| Hydrophis schistosus | Neonate | 0.0384615 | 0.0147059 | NA | 0.0189777 | 0.8904305 |
Age structure does not change significantly over a four year period from 2016 to 2019.
library(car)
snakes%>%
filter(Year != "2019")%>%
group_by(Species)%>%
select(Year, Snout.to.Vent..cm.)%>%
nest()%>%
mutate(mod = map(data, ~lm(Snout.to.Vent..cm. ~ Year, data = .)),
sumr = map(mod, broom::tidy),
stat = map(mod, car::Anova))%>%
select(stat)%>%
unnest()| Species | Sum Sq | Df | F value | Pr(>F) |
|---|---|---|---|---|
| Hydrophis schistosus | 9866.294 | 2 | 21.86203 | 0.00e+00 |
| Hydrophis schistosus | 168785.548 | 748 | NA | NA |
| Hydrophis curtus | 2641.334 | 2 | 10.07170 | 7.05e-05 |
| Hydrophis curtus | 24258.408 | 185 | NA | NA |
GLM shows significance but how do you interpret these results?
# plotting distribution of SVL across months
month.svl <- HS%>%
filter(Year == "2018", # data not sufficient for other years
Snout.to.Vent..cm. > 20)%>% # removing erroneous data
mutate(Month = factor(Month, levels = month.name))%>%
complete(Month)%>%
group_by(Month)%>%
summarise(mean.SVL = mean(Snout.to.Vent..cm., na.rm = T))
births <- data.frame(Species = "Hydrophis schistosus", Month = "April")
HS%>%
filter(Year == "2018", # data not sufficient for other years
Snout.to.Vent..cm. > 20)%>% # removing erroneous data
mutate(Month = factor(Month, levels = month.name))%>%
complete(nesting(Species), Month)%>%
droplevels()%>%
ggplot(aes(Month, Snout.to.Vent..cm.))+
geom_violin(fill = "light grey")+
#geom_point(data = month.svl, aes(x = Month, y = mean.SVL), size = 3)+
geom_boxplot(width = 0.1)+
geom_segment(data = births, aes(x = Month, xend = Month, y = 0 , yend = 10), #marking births
arrow = arrow(length = unit(0.25, "cm"), ends = "first"), size = 1)+
geom_text(data = births, aes(x = Month, y = 20, label = paste("Observed \n births")))+
geom_vline(aes(xintercept = "June"), size = 1)+#start of the monsoon ban
geom_vline(aes(xintercept = "August"), size = 1)+#end of the monsoon ban
geom_text(aes(x = "July", y = 80, label = "Monsoon ban"))+
scale_x_discrete(guide = guide_axis(n.dodge = 2))+
labs(y = "Snout to vent length (cm)")
Proportion of neonates increases in March to May. Birth and neonates were observed around the same time.
- GLM testing change in proportion of SVL < 40 over months
- Other ways to test age structure?
# proportion of jeuveniles in each month
snakes%>%
filter(Year == "2018", # data not sufficient for other years
Snout.to.Vent..cm. > 20)%>% # removing erroneous data
mutate(Month = factor(Month, levels = month.name))%>%
group_by(Species, Month)%>%
summarise(prop.neonate = sum(Snout.to.Vent..cm. < 40)/n())%>%
spread(Month, prop.neonate)| Species | January | February | March | April | May | October | November | December |
|---|---|---|---|---|---|---|---|---|
| Hydrophis curtus | 0 | 0 | 0.50 | NA | NA | 0 | 0.0769231 | 0 |
| Hydrophis schistosus | 0 | 0 | 0.02 | 0.05 | 0.0555556 | 0 | 0.0000000 | 0 |
1.3 Sex ratios
# Plotting sex ratios across years
snakes%>%
filter(Sex == "Male" | Sex == "Female")%>%
group_by( Species, Year, Month)%>%
summarise(N = n(),
females = sum(Sex == "Female"),
prop.female = females/N)%>%
mutate(Month = factor(Month, levels = month.name))%>%
complete(Month)%>%
ggplot(aes(Month, prop.female))+
geom_col(width = 0.5, col = "black")+
scale_x_discrete(guide = guide_axis(n.dodge = 2))+
facet_grid(Year ~ Species)
Proportion of females to entire population stays constant over the sampling period.
snakes%>%
group_by(Species)%>%
filter(Sex == "Male" | Sex == "Female")%>%
summarise(females = sum(Sex == "Female"),
N = n())| Species | females | N |
|---|---|---|
| Hydrophis curtus | 102 | 212 |
| Hydrophis schistosus | 304 | 618 |
# is proportion of females different from 0.5?
snakes%>%
filter(Sex == "Male" | Sex == "Female")%>%
group_by(Species)%>%
summarise(females = sum(Sex == "Female"),
N = n())%>%
group_by(Species)%>%
nest()%>%
mutate(test = map(data, ~prop.test(.$females, .$N, p = 0.5)),
sumr = map(test, broom::tidy))%>%
select(sumr)%>%
unnest()| Species | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| Hydrophis curtus | 0.4811321 | 0.2311321 | 0.6306857 | 1 | 0.4125066 | 0.5504525 | 1-sample proportions test with continuity correction | two.sided |
| Hydrophis schistosus | 0.4919094 | 0.1310680 | 0.7173273 | 1 | 0.4518628 | 0.5320580 | 1-sample proportions test with continuity correction | two.sided |
Sex ratio is not different from 0.5 p = 0.71
2 Observed reproductive cycle of H. schistosus
# percentage of gravid females in sample
HS%>%
count(Gravid)%>%
mutate(N = sum(n))%>%
mutate(prop.gravid = n/N)%>%
filter(Gravid == "Yes")| Gravid | n | N | prop.gravid |
|---|---|---|---|
| Yes | 88 | 967 | 0.0910031 |
Ì¥
# checking the number of gravid females per year
HS%>%
group_by(Year)%>%
filter(Gravid == "Yes")%>%
count(Gravid)%>%
spread(Gravid, n)| Year | Yes |
|---|---|
| 2016 | 1 |
| 2018 | 75 |
| 2019 | 12 |
Proper sampling was only conducted in 2018/19 and hence only this period is used for analysis of reproductive cycles.
# calculating the proportion of gravid females per month
gravid <- HS%>%
mutate(Month = factor(Month, levels = month.name))%>%
filter(Year == "2018" | Year == "2019")%>% # only for 2018/19
group_by(Month)%>%
summarise(N = n(),
gravid = sum(Gravid == "Yes"),
prop.gravid = gravid/N)
print(gravid)## # A tibble: 8 x 4
## Month N gravid prop.gravid
## <fct> <int> <int> <dbl>
## 1 January 102 12 0.118
## 2 February 134 34 0.254
## 3 March 129 24 0.186
## 4 April 54 7 0.130
## 5 May 36 1 0.0278
## 6 October 16 0 0
## 7 November 69 3 0.0435
## 8 December 34 6 0.176Describing change in the relative proportions of gravid females across months and years of sampling.
# plotting prop gravid per month
gravid%>%
mutate(Month = factor(Month, levels = month.name))%>%
complete(Month, fill = list(prop.gravid = 0))%>%
ggplot(aes(Month, prop.gravid))+
geom_point(size = 3)+
geom_path(aes(group = 1), size = 1, linetype = "dashed")+
geom_segment(aes(x = "April", xend = "April", y = 0 , yend = 0.02), #marking births
arrow = arrow(length = unit(0.25, "cm"), ends = "first"), size = 1)+
geom_text(aes(x = "April", y = 0.04, label = paste("Observed \n births")))+
geom_vline(aes(xintercept = "June"), size = 1)+#start of the monsoon ban
geom_vline(aes(xintercept = "August"), size = 1)+#end of the monsoon ban
geom_text(aes(x = "July", y = 0.20, label = "Monsoon ban"))+
scale_x_discrete(guide = guide_axis(n.dodge = 2))+
labs(y = "Proportion of gravid females")
Pregnancy from Novemeber to May. Peak in Feb. Observed two live births in April.
- Anova/GLM with change in proportion of gravid females over time.
3 Development of embryos and eggs
3.1 Sample size
embryos%>%
summarise(n.mothers = length(unique(Field.Code)),
n.embryos = length(unique(Embryo.Code)))| n.mothers | n.embryos |
|---|---|
| 29 | 235 |
3.2 Summary
embryos%>%
select(Egg.Length..mm.., Egg.Width..mm.., Egg.Weigth..g.., Snout.to.Vent..cm., Embryo.Weight..g.)%>%
skimr::skim()%>%
skimr::yank("numeric")%>%
select(-hist)Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| Egg.Length..mm.. | 80 | 0.66 | 39.71 | 11.72 | 2.23 | 31.66 | 37.82 | 47.54 | 73.98 |
| Egg.Width..mm.. | 80 | 0.66 | 32.23 | 9.01 | 2.02 | 25.56 | 33.72 | 37.64 | 53.09 |
| Egg.Weigth..g.. | 51 | 0.78 | 14.30 | 5.45 | 4.23 | 10.75 | 14.00 | 17.25 | 31.00 |
| Snout.to.Vent..cm. | 111 | 0.53 | 17.72 | 5.24 | 6.30 | 14.38 | 16.40 | 21.08 | 30.40 |
| Embryo.Weight..g. | 66 | 0.72 | 4.83 | 6.28 | 0.00 | 0.00 | 3.00 | 8.00 | 20.44 |
embryos%>%
group_by(Field.Code)%>%
filter(Sex != "")%>%
summarise(prop.female = sum(Sex == "Female")/n())%>%
skimr::skim(prop.female)%>%
skimr::yank("numeric")%>%
select(-hist)Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| prop.female | 0 | 1 | 0.53 | 0.31 | 0 | 0.31 | 0.46 | 0.71 | 1 |
embryos%>%
filter(Sex != "")%>%
summarise(females = sum(Sex == "Female"),
N = n())| females | N |
|---|---|
| 86 | 166 |
broom::tidy(prop.test(86, 166, p = 0.5))%>%
select(estimate:conf.high)| estimate | statistic | p.value | parameter | conf.low | conf.high |
|---|---|---|---|---|---|
| 0.5180723 | 0.1506024 | 0.6979603 | 1 | 0.4395567 | 0.5957383 |
The sex ratio in clutches is not significantly different from 0.5.
3.3 Infertility
embryos%>%
count(Embryo)%>%
mutate(N = sum(n), rate = n*100/N)| Embryo | n | N | rate |
|---|---|---|---|
| Absent | 5 | 235 | 2.12766 |
| Present | 230 | 235 | 97.87234 |
3.4 Matrotrophy
Do female H. schistosus expend energy in the development of embryos? or Are the eggs formed and yolk only provides nourishment?
embryos%>%
ggplot(aes(Embryo.Weight..g., Yolk.Weight..g.))+
geom_point(size = 3)+
geom_smooth(method = "lm", linetype = "dashed", size = 1)
embryos%>%
select(Yolk.Weight..g., Embryo.Weight..g.)%>%
nest()%>%
mutate(mod = map(data, ~lm(Yolk.Weight..g. ~ Embryo.Weight..g., data = .)),
sumr = map(mod, broom::tidy),
stat = map(mod, broom::glance))%>%
select(sumr, stat)%>%
unnest()%>%
select(term:r.squared)| term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|
| (Intercept) | 13.0905565 | 0.2965229 | 44.14687 | 0 | 0.4071222 |
| Embryo.Weight..g. | -0.4526283 | 0.0450509 | -10.04705 | 0 | 0.4071222 |
Yolk weight decreases as embryo weight increases.
embryos%>%
ggplot(aes(Embryo.Weight..g., Egg.Weigth..g..))+
geom_point(aes(col = Yolk.Weight..g.), size = 3)+
geom_smooth(method = "lm", linetype = "dashed", size = 1)+
scale_color_viridis(name = "Yolk Weight (g)")+
labs(x = "Embryo Weight (g)", y = "Total Egg Weight (g)")
embryos%>%
select(Egg.Weigth..g.., Embryo.Weight..g.)%>%
nest()%>%
mutate(mod = map(data, ~lm(Egg.Weigth..g.. ~ Embryo.Weight..g., data = .)),
sumr = map(mod, broom::tidy),
stat = map(mod, broom::glance))%>%
select(sumr, stat)%>%
unnest()%>%
select(term:r.squared)| term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|
| (Intercept) | 13.5441434 | 0.4114909 | 32.91481 | 0 | 0.2188866 |
| Embryo.Weight..g. | 0.3676922 | 0.0557912 | 6.59050 | 0 | 0.2188866 |
Egg weight increases as embryo weight increases
Decrease of fat bodies as embryos develop indicates matrotrophic nutrition.
- Photos of females dissected ordered according to embryo dev stage
4 Reproductive effort
# Cleaning reproductive effort data
re <- embryos%>%
left_join(snakes, by = c("Date", "Field.Code", "Species"))%>%
select(Date, Field.Code, Embryo.Code, Species, Egg.Length..mm..:Tail.Length..cm..x,
Egg.Weigth..g..:Sex.x, Snout.to.Vent..cm..y:Tail.Length..cm..y, Weight..g.,
-Body.Length..cm..x, - Body.Length..cm..y)%>%
filter(Species == "Hydrophis schistosus")%>%
rename(Embryo.SVL = Snout.to.Vent..cm..x,
Embryo.TL = Tail.Length..cm..x,
Embryo.Sex = Sex.x,
Female.SVL = Snout.to.Vent..cm..y,
Female.TL = Tail.Length..cm..y,
Mother.Wt = Weight..g.
)%>%
filter(Female.SVL > 50)4.1 Increase in clutch size with female age
Does the amount of enery expended by female H. schistosus reduce with age?
re_clutch <- re%>%
select(Field.Code, Egg.Weigth..g.., Mother.Wt, Female.SVL)%>%
group_by(Field.Code)%>%
summarise(clutch.size = n(),
Clutch.wt = sum(Egg.Weigth..g..),
Mother.wt = last(Mother.Wt),
Female.SVL = last(Female.SVL))%>%
mutate(rcm = Clutch.wt/(Mother.wt - Clutch.wt))re_clutch%>%
ggplot(aes(Female.SVL, clutch.size))+
geom_point(size =3)+
geom_smooth(method = "lm")
re_clutch%>%
select(Female.SVL, clutch.size)%>%
nest()%>%
mutate(mod = map(data, ~lm(clutch.size ~ Female.SVL, data = .)),
sumr = map(mod, broom::tidy),
stat = map(mod, broom::glance))%>%
select(sumr, stat)%>%
unnest()%>%
select(term:r.squared)| term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|
| (Intercept) | -12.0816516 | 4.4876375 | -2.692208 | 0.0154238 | 0.5466726 |
| Female.SVL | 0.2134755 | 0.0471483 | 4.527747 | 0.0002976 | 0.5466726 |
The number of eggs borne by females increases with age (SVL).
4.2 Change in overall reproductive effort with age
re_clutch%>%
ggplot(aes(Female.SVL, rcm))+
geom_point(aes(col = clutch.size), size = 3)+
geom_smooth(method = "lm", linetype = "dashed")+
scale_y_continuous(name = "Relative clutch mass")+
scale_x_continuous(limits = c(85, 115), name = "Female SVL (cm)")+
scale_color_viridis(name = "Clutch size")+
theme(legend.text = element_text(size = 10))
re_clutch%>%
select(Female.SVL, rcm)%>%
nest()%>%
mutate(mod = map(data, ~lm(rcm ~ Female.SVL, data = .)),
sumr = map(mod, broom::tidy),
stat = map(mod, broom::glance))%>%
select(sumr, stat)%>%
unnest()%>%
select(term:r.squared)| term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|
| (Intercept) | 0.6886163 | 0.4077131 | 1.6889727 | 0.1170168 | 0.063695 |
| Female.SVL | -0.0038362 | 0.0042458 | -0.9035133 | 0.3840327 | 0.063695 |
The overall reproductive effort does not change with female age.
4.3 Change in reproductive effort per embryo with female age
Does the effort per embryo change with female age?
# reproductive effort per embruo
re_embryo <- re%>%
select(Field.Code, Embryo.Code, Egg.Weigth..g.., Mother.Wt, Female.SVL, Embryo.Sex, Embryo.SVL)%>%
group_by(Field.Code)%>%
mutate(clutch.wt = sum(Egg.Weigth..g..),
Female.wt = Mother.Wt - clutch.wt)%>%
group_by(Field.Code, Embryo.Code)%>%
summarise(inv = Egg.Weigth..g../Female.wt,
Female.SVL = last(Female.SVL),
Embryo.Sex = Embryo.Sex,
Embryo.SVL = Embryo.SVL)%>%
ungroup()# using residuals to control for embryo development
emsvlinv <- lm(inv ~ Embryo.SVL, data = re_embryo)
re_embryo%>%
modelr::add_residuals(emsvlinv)%>%
ggplot(aes(Female.SVL, resid))+
geom_point(aes(col = Embryo.SVL), size = 3)+
geom_smooth(method = "lm", linetype = "dashed", size = 1)+
scale_y_continuous(name = "Relative egg mass (residuals)")+
scale_x_continuous(limits = c(85, 115), name = "Female SVL (cm)")+
scale_color_viridis(name = "Embryo SVL (cm)")
re_embryo%>%
select(Female.SVL, inv, Embryo.SVL)%>%
nest()%>%
mutate(mod = map(data, ~lm(inv ~ Female.SVL + Embryo.SVL, data = .)),
sumr = map(mod, broom::tidy),
stat = map(mod, broom::glance))%>%
select(sumr, stat)%>%
unnest()%>%
select(term:p.value, r.squared)| term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|
| (Intercept) | 0.0858802 | 0.0260524 | 3.296444 | 0.0017188 | 0.4433341 |
| Female.SVL | -0.0007894 | 0.0002588 | -3.050593 | 0.0035109 | 0.4433341 |
| Embryo.SVL | 0.0023248 | 0.0004171 | 5.573820 | 0.0000008 | 0.4433341 |
The relative egg mass (controlled for embryo development) reduces with female SVL.
4.4 Difference in reproductive effort for male and female embryos
re_embryo%>%
filter(Embryo.Sex == "Male" | Embryo.Sex == "Female")%>%
modelr::add_residuals(emsvlinv)%>%
ggplot(aes(Female.SVL, resid, shape = Embryo.Sex))+
geom_point(aes(col = Embryo.SVL), size = 3)+
geom_smooth(method = "lm", linetype = "dashed", size = 1)+
scale_y_continuous(name = "Relative egg mass (residuals)")+
scale_x_continuous(limits = c(85, 115), name = "Female SVL (cm)")+
scale_color_viridis(name = "Embryo SVL (cm)")
re_embryo%>%
filter(Embryo.Sex == "Male" | Embryo.Sex == "Female")%>%
select(Embryo.Sex, inv, Embryo.SVL)%>%
nest()%>%
mutate(mod = map(data, ~lm(inv ~ Embryo.Sex + Embryo.SVL, data = .)),
sumr = map(mod, broom::tidy),
stat = map(mod, broom::glance))%>%
select(sumr, stat)%>%
unnest()%>%
select(term:p.value, r.squared)| term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|
| (Intercept) | 0.0165286 | 0.0090610 | 1.824151 | 0.0735636 | 0.3859103 |
| Embryo.SexMale | -0.0083114 | 0.0045803 | -1.814613 | 0.0750384 | 0.3859103 |
| Embryo.SVL | 0.0022764 | 0.0004455 | 5.109887 | 0.0000042 | 0.3859103 |
5 Mortality in bycatch
5.1 Overall mortality
snakes%>%
group_by(Species)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)| Species | n | N | prop.dead |
|---|---|---|---|
| Hydrophis curtus | 120 | 282 | 0.4255319 |
| Hydrophis schistosus | 174 | 967 | 0.1799380 |
snakes%>%
group_by(Species)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)%>%
ungroup()%>%
nest()%>%
mutate(test = map(data, ~prop.test(.$n, .$N)),
sumr = map(test, broom::tidy))%>%
select(sumr)%>%
unnest()%>%
select(estimate1:p.value)| estimate1 | estimate2 | statistic | p.value |
|---|---|---|---|
| 0.4255319 | 0.179938 | 71.81006 | 0 |
5.2 Mortality by age class
snakes%>%
filter(!is.na(Class))%>%
group_by(Species, Class)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)| Species | Class | n | N | prop.dead |
|---|---|---|---|---|
| Hydrophis curtus | Adult | 40 | 62 | 0.6451613 |
| Hydrophis curtus | Juvenile | 41 | 125 | 0.3280000 |
| Hydrophis schistosus | Adult | 138 | 648 | 0.2129630 |
| Hydrophis schistosus | Juvenile | 5 | 105 | 0.0476190 |
| Hydrophis schistosus | Neonate | 3 | 8 | 0.3750000 |
snakes%>%
filter(!is.na(Class))%>%
group_by(Species, Class)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)%>%
group_by(Species)%>%
nest()%>%
mutate(test = map(data, ~prop.test(.$n, .$N)),
sumr = map(test, broom::tidy))%>%
select(sumr)%>%
unnest()%>%
select(estimate1:p.value)| Species | estimate1 | estimate2 | statistic | p.value |
|---|---|---|---|---|
| Hydrophis curtus | 0.6451613 | 0.328000 | 15.71186 | 0.0000738 |
| Hydrophis schistosus | 0.2129630 | 0.047619 | 17.68174 | 0.0001447 |
5.3 Mortality by sex
snakes%>%
filter(Sex != "")%>%
group_by(Species, Sex)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)| Species | Sex | n | N | prop.dead |
|---|---|---|---|---|
| Hydrophis curtus | Female | 36 | 102 | 0.3529412 |
| Hydrophis curtus | Male | 52 | 110 | 0.4727273 |
| Hydrophis schistosus | Female | 62 | 304 | 0.2039474 |
| Hydrophis schistosus | Male | 72 | 314 | 0.2292994 |
snakes%>%
filter(Sex != "")%>%
group_by(Species, Sex)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)%>%
group_by(Species)%>%
nest()%>%
mutate(test = map(data, ~prop.test(.$n, .$N)),
sumr = map(test, broom::tidy))%>%
select(sumr)%>%
unnest()%>%
select(estimate1:p.value)| Species | estimate1 | estimate2 | statistic | p.value |
|---|---|---|---|---|
| Hydrophis curtus | 0.3529412 | 0.4727273 | 2.6538718 | 0.1032980 |
| Hydrophis schistosus | 0.2039474 | 0.2292994 | 0.4448479 | 0.5047918 |
5.4 Mortality by female reproductive status
HS%>%
filter(Sex == "Female",
Class == "Adult")%>%
group_by(Gravid)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)| Gravid | n | N | prop.dead |
|---|---|---|---|
| 41 | 151 | 0.2715232 | |
| Yes | 16 | 85 | 0.1882353 |
HS%>%
filter(Sex == "Female",
Class == "Adult")%>%
group_by(Gravid)%>%
count(Condition.at.encounter..D.A.)%>%
mutate(N = sum(n),
prop.dead = n/N)%>%
filter(Condition.at.encounter..D.A. == "Dead")%>%
select(-Condition.at.encounter..D.A.)%>%
ungroup()%>%
nest()%>%
mutate(test = map(data, ~prop.test(.$n, .$N)),
sumr = map(test, broom::tidy))%>%
select(sumr)%>%
unnest()%>%
select(estimate1:p.value)| estimate1 | estimate2 | statistic | p.value |
|---|---|---|---|
| 0.2715232 | 0.1882353 | 1.629856 | 0.2017229 |